Figure 14 shows annual mean all-sky TOA DRE and first AIE due to residential emissions for the baseline simulation.
-200 -160 -120 -80 -40 0.0 40 80 120 160 200
Direct Radiative Effect (mW m-2) -200 -160 -120 -80 -40 0.0 40 80 120 160 200
First Aerosol Indirect Effect (mW m-2)
1
Figure 14. Annual mean all-sky direct radiative effect (DRE) (left panel) and first aerosol indirect effect (AIE) (right panel) due to residential emissions (res_base), relative to an equivalent simulation where residential emissions have been removed (res_base_off).
Figure 15. Global annual mean all-sky direct radiative effect (DRE) (red) and first aerosol indirect effect (AIE) (blue) for all model sim-ulations due to the impact of residential combustion emission, rel-ative to simulations where residential combustion emissions have been removed. DRE and AIE values for each simulation are detailed in Table 2.
Residential emissions result in a negative (cooling) annual mean DRE over large regions of South Asia, East Asia, sub-Saharan Africa, and parts of southern Europe, with values as large as−200 mW m−2. The simulated net negative DRE in South Asia and East Asia is consistent with a previous study (Aunan et al., 2009). In contrast, over parts of Eastern Europe and the Russian Federation, North Africa, the Middle East, and Southeast Asia, residential emissions lead to a positive DRE. Residential emissions cause a negative first AIE over most regions, with values as large as −200 mW m−2 over eastern Africa, Eastern Europe, and West Africa. Small posi-tive AIE (<40 mW m−2) is simulated in the remote Southern Ocean due to reductions in CDNC as mentioned in Sect. 3.5.
Figure 15 compares the annual mean all-sky DRE and first AIE across the different model simulations (also re-ported in Table 2). The simulated global annual mean DRE has an uncertain sign, with our estimates between−66 and
+85 mW m−2. The baseline simulation results in a global mean DRE of −5 mW m−2, similar to the simulation us-ing monthly varyus-ing emissions (−8 mW m−2). Our estimates differ somewhat to Kodros et al. (2015), who found a ho-mogeneous optical mixing state produced a positive DRE of +15 mW m−2 for biofuel emissions; however, because res-idential emissions differ to biofuel emissions, comparisons become problematic. We therefore assume that differences in radiative effect compared to Kodros et al. (2015) are likely dominated by differences in emissions used and differences in the optical calculation. Doubling residential carbonaceous emissions, but keeping SO2 emissions constant, results in a positive global annual mean DRE (+21 mW m−2for res_×2 and+10 mW m−2for res_monthly_×2). This suggests that the carbonaceous (BC and POM) component of residen-tial aerosol in our model exerts a positive DRE, but this is offset by cooling from SO2 emissions. Doubling only BC emissions leads to a stronger positive DRE (+85 mW m−2), whereas negative DRE are simulated for doubling only POM (−66 mW m−2) or SO2(−43 mW m−2) emissions. The DRE is also sensitive to emitted particle size, resulting in positive global mean DRE of between+1 and+63 mW m−2 when carbonaceous particles are emitted at smaller sizes (res_aero and res_small respectively). This change in sign to a posi-tive DRE can be attributed to reduced removal rates for car-bonaceous particles emitted at smaller sizes, which leads to larger BC burden, particularly in the FT where BC influ-ence on DRE is most efficient. Residential emissions ex-ert a negative (cooling) but uncex-ertain global annual mean first AIE, estimated at between −502 and −16 mW m−2. The baseline simulation results in a global mean first AIE of −25 mW m−2, similar to the simulation using monthly varying emissions (−20 mW m−2). Emitting residential car-bonaceous aerosol at small sizes contributes most of the uncertainly to simulated first AIE, with estimates between
−46 mW m−2(res_aero) and−502 mW m−2(res_small) due to a greater increase in global CDNC. We find little sen-sitivity of the AIE to changes in carbonaceous emission mass: doubling carbonaceous emissions (res_×2) changes
AIE by less than 2 mW m−2(∼10 %) due to limited changes in CDNC. In contrast, doubling SO2emissions leads to the greater negative AIE (−45 mW m−2) due to greater global contribution to CDNCs.
4 Discussion and conclusions
We used a global aerosol microphysics model (GLOMAP) to quantify the impacts of residential emissions on ambi-ent aerosol, human health, and climate in the year 2000. We tested the sensitivity of simulated aerosol to uncertainty in emission amount and seasonal variability, emitted primary carbonaceous aerosol size distributions, and the impact of particle formation.
To evaluate model simulations we synthesised in situ ob-servations of BC, OC, and PM2.5concentrations and aerosol number size distribution. The baseline simulation underes-timated observed BC, OC, and PM2.5 concentrations, with the largest underestimation over East Asia and South Asia, consistent with other modelling studies (Fu et al., 2012;
Moorthy et al., 2013; Pan et al., 2015). Applying monthly varying emissions (MACCity emission data set), in place of annual mean emissions (ACCMIP emission), has little im-provement on overall model bias but does improve the abil-ity of the model to simulate the observed seasonal variabil-ity of aerosol. Doubling residential carbonaceous combus-tion emissions improved model agreement, but GLOMAP still underestimated BC, OC, and PM2.5concentrations. The model typically had a larger underestimation of OC com-pared to BC concentrations, possibly due to uncertainty in emission factors or potentially due to an underestimation of anthropogenic SOA (Spracklen et al., 2011b).
We used source apportionment studies using 14C non-fossil BC analysis at the island site of Hanimaadhoo in the Indian Ocean as an additional constraint of the model. Non-fossil sources have been estimated to contribute 46–73 % at this location. This large range makes it difficult to constrain the model. With standard emissions (ACCMIP and MACC-ity), we estimate a non-fossil fraction of 57–65 %, whereas when residential BC emissions are doubled, we simulate a non-fossil fraction of 72–79 %.
Overall, our results suggest that residential emissions may be underestimated in the MACCity and ACCMIP data sets.
Uncertainty in aerosol removal processes and transport and missing anthropogenic SOA and nitrate formation may all contribute to underestimation of aerosol mass. Nevertheless, previous modelling studies have also suggested that residen-tial emission data sets underestimate emissions (Park et al., 2005; Koch et al., 2009; Ganguly et al., 2009; Menon et al., 2010; Bergström et al., 2012; Nair et al., 2012; Fu et al., 2012; Moorthy et al., 2013; Bond et al., 2013; Pan et al., 2015). The ACCMIP and MACCity emission data sets are constructed using national data on fuel use, which implies uniform per capita fuel consumption at the country level.
Us-ing subnational fuel use data, R. Wang et al. (2014) showed that the MACCity data set underestimated residential emis-sions over source regions in Asia. Other studies have also had to increase residential emissions over Europe in order to match source apportionment studies (Denier van der Gon et al., 2015). However, Wang et al. (2013) suggested that model bias over China could partly be attributed to coarse model resolution and comparison against urban data and monthly mean observations. Kumar et al. (2015) also showed that a high-resolution model was able simulate reasonable BC dis-tributions in South Asian region. We have restricted our anal-ysis to rural and background sites but use monthly mean BC and OC data and a relatively coarse-resolution global model. To help resolve uncertainties in residential emission budget, higher-resolution emission inventories (using sub-national fuel use data) and higher-resolution model simula-tions evaluated against long-term and high temporal resolu-tion data are required. In many regions, observaresolu-tional data are lacking; there is an urgent requirement for detailed char-acterisation of the chemical, physical, and optical properties of aerosol in regions impacted by residential emissions, par-ticularly in the developing world.
Particle number concentrations are generally predicted within a factor of 2 at the limited number of locations where observations are available. Simulated particle number is very sensitive to emitted particle size, which has a large un-certainty. Emitting residential carbonaceous particles at the small end of the range reported by Bond et al. (2006) (ge-ometric mean diameter=20 nm) substantially overestimates observed particle number, suggesting this assumption is not appropriate for coarse-resolution global models.
Residential emissions contribute substantially to simulated annual mean surface PM concentrations. Greatest fractional contributions (15 to >40 %) to surface PM2.5 concentra-tions are simulated over Eastern Europe (including parts of the Russian Federation), parts of East Africa, South Asia, and East Asia. In these regions residential emissions con-tribute>50 % to total simulated BC and POM concentra-tions. These findings support previous studies suggesting a large contribution of residential emissions to PM2.5 concen-trations over Asia (Venkataraman et al., 2005; Cao et al., 2006; Klimont et al., 2009; Lei et al., 2011; Cui et al., 2015;
Fu et al., 2012; Gustafsson et al., 2009; B. Chen et al., 2013).
Our findings suggest that reductions in residential emissions need to be considered alongside mitigation strategies for other PM sources (e.g. industry and transport) within Asia and in even more developed regions such as parts of Europe (Fountoukis et al., 2014).
We estimated the impact of residential emissions on hu-man health due to increased ambient PM2.5 concentrations and tested the sensitivity to the emission data set and emis-sion budget. We used a log-linear model of relative risk from the epidemiological literature (Ostro, 2004) to relate simulated changes in ambient PM2.5concentrations to long-term excess premature mortality for cardiopulmonary
dis-ease and lung cancer for adults (>30 years of age). In the baseline simulation, we estimate that residential emissions cause 315 000 (132 000–508 000, 5th to 95th percentile un-certainty range) premature mortalities each year. Applying a seasonal cycle to emissions changed our estimate by less than 2 %, with residential emissions resulting in 308 000 (113 300–497 000) premature mortalities each year. Our es-timate for residential emissions is equivalent to 8 % of the total mortality attributed to exposure to ambient PM2.5from all anthropogenic sources (WHO, 2014b), although we note that methodologies in the two studies are different. Doubling residential carbonaceous emissions, which improved model comparison against observed BC and POM concentrations, increases simulated excess mortality by∼64 % to 516 600 (192 000–827 000). Simulated mortality is greatest over re-gions with large residential emissions and high population densities including East Asia, South Asia, Eastern Europe, and the Russian Federation. We find that half of simulated global excess mortality from residential emissions occurs in China and India alone. Our results are consistent with a pre-vious estimate of RSF cooking emissions on premature mor-tality (Chafe et al., 2014). The CRFs that are used to esti-mate long-term premature mortality are uncertain. The log-linear function used here is based on epidemiological stud-ies from North America (Pope III et al., 2002), resulting in greater uncertainty when these functions are extrapolated to other regions (Silva et al., 2013). However, epidemiological studies are not available for all regions, so global mortality estimates often use functions based on these North Amer-ican studies. Overall, we find that uncertainty in the rela-tionship between PM concentrations and health impacts (as quantified by the 95th percentile range given by the log-linear model) and our measure of uncertainty in emissions (esti-mated here as a factor of 2 uncertainty) result in compara-ble uncertainty in the estimated global number of premature mortalities. Future work therefore needs to improve both our understanding of residential emissions and the relationships between enhanced PM concentrations and human health im-pacts. We also note that the coarse resolution of our global model likely provides a conservative estimate of premature mortality due to residential emissions because it cannot simu-late high concentrations associated with highly popusimu-lated ur-ban and semi-urur-ban areas. Further simulations using higher-resolution models and emission inventories will be required to accurately simulate PM2.5 concentrations in urban and semi-urban areas. Health effects using more recent CRFs that relate RR of disease to changes in PM2.5over a large range of concentration exposures (Burnett et al., 2014) will also be re-quired. In addition, exposure functions, such as the one used in this study, treat all aerosol components as equally toxic, but carbonaceous aerosol, which dominate residential emis-sions, may be more toxic compared to inorganic or crustal PM (Tuomisto et al., 2008). New exposure response func-tions will therefore need to account for the different toxicity of chemical components present in atmospheric aerosols.
We used an offline radiative transfer model to estimate the radiative effect (RE) of aerosol from residential emissions.
We estimate that residential emissions exert a global annual mean DRE of between−66 and+85 mW m−2. The simu-lated global mean DRE is sensitive to the amount and ratio of BC, POM, and SO2in emissions. Doubling residential car-bonaceous emissions, but keeping SO2emissions constant, results in a positive global annual mean DRE, suggesting that the carbonaceous component of residential aerosol exerts a net positive DRE in our simulations, offset by cooling from SO2emissions. We also find a positive DRE when primary carbonaceous emissions are emitted at smaller sizes, but this simulation overestimates observed aerosol number, suggest-ing it is unrealistic. Discountsuggest-ing this simulation, we provide a best estimate of global mean DRE due to residential com-bustion of between−66 and+21 mW m−2for the year 2000.
Residential emissions exert a simulated global annual mean first AIE of between −502 and −16 mW m−2. Un-certainty in emitted primary carbonaceous particle size con-tributes most of the uncertainly to calculated AIE. Emitting carbonaceous aerosol at smaller sizes results in greater simu-latedN50and CDNC and a strong negative AIE as well as in overestimation of observed particle number, suggesting that emission at very small sizes is not realistic. We find little sensitivity to annual mean first AIE due changes in carbona-ceous emission mass compared to the baseline simulation.
Doubling carbonaceous emissions changes AIE by less than 2 mW m−2 (∼10 %), highlighting a non-linear relationship between magnitude of emission and first AIE. Our best esti-mate of the first AIE due to residential emissions is between
−52 and−16 mW m−2in the year 2000.
We have restricted our analysis of the RE of residential emissions to the aerosol DRE and first AIE. We treat POM aerosol as scattering, although a fraction of POM aerosol may absorb radiation (Kirchstetter et al., 2004; Chen and Bond, 2010; Arola et al., 2011; X. Wang et al., 2014). Fur-thermore, our DRE analysis is limited because we do not fully explore the full range of optical mixing states for res-idential emissions. We assume that BC is mixed homoge-neously with scattering species, which provides an upper limit for BC DRE (Jacobson, 2001). A full investigation of the different optical mixing states commonly used in global models, such as in Kodros et al. (2015), would yield a better understanding of DRE from residential emissions. Because we use an offline radiative transfer model, we also do not treat cloud lifetime (second indirect effect) or semi-direct ef-fects (Koch and Del Genio, 2010) and cannot explore ad-ditional impacts such as the weakening of the South Asia monsoon, altering of precipitation patterns (Ramanathan et al., 2005), tropical cyclone intensification (Evan et al., 2011), and accelerated melting of glaciers in the Himalayas (Xu et al., 2009).
The introduction of cleaner and fuel efficient residential combustion technologies, processed solid fuels, and clean al-ternative energy (e.g. natural gas, electricity) has been
sug-gested as one of the fastest ways to reduce residential emis-sions (UNEP, 2011), thus slowing climate change and im-proving air quality and human health (WHO, 2009). Our study shows that the complete elimination of residential emissions would result in substantially improved PM air quality and human health across large regions of the world regardless of the uncertainties between the different model simulations explored here.
We have shown that residential combustion emissions ex-ert an uncex-ertain RE, which leads to uncex-ertainties in predicting the climate impact of emission reductions. Our work sug-gests that residential emission flux, chemical composition, and carbonaceous size distributions need to be better charac-terised in order to constrain the likely climate impact. Given these uncertainties, the missing processes within our model framework (described above), and the use of an offline radia-tive transfer model, it is difficult asses the full climate im-pacts due to residential emissions. In addition, because we find residential emission amount and resulting RE (particu-larly aerosol–cloud effects) are not linearly related, our re-sults cannot be used to estimate the impacts associated with smaller, realistic reductions in residential emissions. Future research is needed to explore the air quality and climate im-pact of realistic emission reductions scenarios that could po-tentially be achieved through the implementation of cleaner combustion technologies and clean alternative fuels.
More people are using RSF for cooking than at any other point in human history, even though the fraction of the popu-lation using these fuels is falling (Bonjour et al., 2013). Over the next few decades (2005–2030), combustion of RSF is projected to increase in South Asia and Africa due to in-creases in human population (UNEP, 2011). We have re-ported human health and climate impacts for the year 2000, but in China, residential emissions have increased 34 % dur-ing the period 2000–2012 due to the growth of coal consump-tion (Cui et al., 2015). The use of biomass for heating is also expected to increase in developed countries such as in West-ern Europe because of rising fossil fuel prices and use of renewable biomass under climate change mitigation policy (Denier van der Gon et al., 2015). The impact of residential emissions on human health and climate is, therefore, likely to persist in the future unless effective mitigation to address the dependence on RSFs is taken.
Appendix A
Table A1. Acronyms used in this study.
Acronym Description
ACCMIP Atmospheric Chemistry and Climate Model Intercomparison Project AF Attributable fraction
AIE Aerosol indirect effect
BC Black carbon
BHN Binary homogenous nucleation BLN Boundary layer nucleation CCN Cloud condensation nuclei
CDNC Cloud droplet number concentration CPD Cardiopulmonary disease
CRF Concentration response functions DRE Direct radiative effect
EC Elemental carbon FT Free troposphere
LC Lung cancer
LPG Liquefied petroleum gas
LW Longwave
MACCity MACC/CityZEN project NH Northern Hemisphere
N3 Number of particles greater than 3 nm dry diameter N50 Number of particles greater than 50 nm dry diameter N100 Number of particles greater than 100 nm dry diameter NMBF Normalised mean bias factor
OC Organic carbon
PM Particulate matter
PM2.5 Particulate matter with an aerodynamic dry diameter of<2.5 µm POM Particulate organic matter
RE Radiative effect
RR Relative risk
RSF Residential solid fuel SOA Secondary organic aerosol
SW Shortwave
TOA Top of atmosphere
Acknowledgements. E. W. Butt acknowledges support from the United Bank of Carbon and the University of Leeds. V. Vakkari acknowledges support from the Academy of Finland Finnish Cen-ter of Excellence program (grant no. 1118615). Ambient aerosol measurements obtained through the Atmospheric Brown Cloud project funded by the United Nations Environmental Programme and the National Oceanic and Atmospheric Administration.
Particulate matter sample collection, analysis, and data validation was supported by James J. Schauer at the University of Wisconsin-Madison, Jeff DeMinter at the Wisconsin State Laboratory of Hygiene, Soon-Chang Yoon of Seoul National University, and Pradeep Dangol and Bidya Banmali Pradhan at the International Center for Integrated Mountain Development. H. Yang would like to thank the support from National Science Foundation for Young Scholars of China (grant no. 41205003). We acknowledge funding from the Natural Environment Research Council (NERC) (grant no. NE/K015966/1).
Edited by: S. S. Gunthe
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